AUTHOR=Thomas Lucy , Hyde Chris , Mullarkey Dan , Greenhalgh Jack , Kalsi Dilraj , Ko Justin 

TITLE=Real-world post-deployment performance of a novel machine learning-based digital health technology for skin lesion assessment and suggestions for post-market surveillance

JOURNAL=Frontiers in Medicine

VOLUME=Volume 10 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2023.1264846

DOI=10.3389/fmed.2023.1264846

ISSN=2296-858X

ABSTRACT=Introduction
Deep Ensemble for Recognition of Malignancy (DERM) is an artificial intelligence as a medical device (AIaMD) tool for skin lesion assessment.
Methods 
We report prospective real-world performance from its deployment within skin cancer pathways at two National Health Service hospitals (UK) between July 2021 and October 2022.
Results
14,500 cases were seen, including patients 18–100 years old with Fitzpatrick skin types I–VI represented. Based on 8,571 lesions assessed by DERM with confirmed outcomes, versions A and B demonstrated very high sensitivity for detecting melanoma (95.0–100.0%) or malignancy (96.0–100.0%). Benign lesion specificity was 40.7–49.4% (DERM-vA) and 70.1–73.4% (DERM-vB). DERM identified 15.0–31.0% of cases as eligible for discharge.
Discussion
We show DERM performance in-line with sensitivity targets and pre-marketing authorisation research, and it reduced the caseload for hospital specialists in two pathways. Based on our experience we offer suggestions on key elements of post-market surveillance for AIaMDs.